Análisis de ingredientes en recetas

Análisis de https://www.nature.com/articles/srep00196.pdf

Podemos usar read_lines_chunked si el archivo original es grande. En este ejemplo, filtramos las recetas East Asian:

library(tidyverse)

limpiar <- function(lineas,...){
  str_split(lineas, ',') %>% 
    keep(~.x[1] == 'EastAsian') %>%
    map(~.x[-1]) %>% # quitar tipo de cocina
    map(~.x[nchar(.x) > 0]) # quitar elementos vacios
}
callback_limpiar <- ListCallback$new(limpiar)
filtrado <- read_lines_chunked('../datos/recetas/srep00196-s3.csv',
                    skip = 1, callback = callback_limpiar, chunk_size = 1000)
recetas <-  filtrado %>% flatten

recetas[1:10]

[[1]]
[1] "beef_broth" "egg"        "soy_sauce"  "soybean"   

[[2]]
[1] "sesame_oil"          "beef"                "roasted_sesame_seed" "matsutake"          
[5] "black_pepper"        "scallion"            "garlic"              "soy_sauce"          

[[3]]
[1] "vinegar"             "roasted_sesame_seed" "cayenne"             "scallion"           
[5] "garlic"              "soybean"             "cucumber"            "rice"               

[[4]]
 [1] "beef"                "roasted_sesame_seed" "soy_sauce"           "cayenne"            
 [5] "ginger"              "scallion"            "lettuce"             "garlic"             
 [9] "vegetable"           "sake"               

[[5]]
[1] "garlic"    "fish"      "cayenne"   "soy_sauce" "potato"   

[[6]]
 [1] "sweet_potato"        "onion"               "roasted_sesame_seed" "soy_sauce"          
 [5] "cayenne"             "ginger"              "soybean"             "vegetable"          
 [9] "cabbage"             "rice"                "chicken"             "sesame_oil"         

[[7]]
 [1] "sesame_oil"          "radish"              "fish"                "black_pepper"       
 [5] "ginger"              "garlic"              "seaweed"             "shrimp"             
 [9] "beef"                "roasted_sesame_seed" "soy_sauce"           "cayenne"            
[13] "chinese_cabbage"     "scallion"            "sesame_seed"         "rice"               

[[8]]
 [1] "vinegar"   "radish"    "fish"      "cayenne"   "scallion"  "cucumber"  "soybean"  
 [8] "vegetable" "garlic"    "rice"      "soy_sauce"

[[9]]
[1] "radish"        "fish"          "cayenne"       "ginger"        "scallion"     
[6] "garlic"        "vegetable_oil" "soy_sauce"    

[[10]]
[1] "nut"         "cucumber"    "sesame_seed" "soybean"    

library(arules)
length(recetas)

[1] 2512

## No hacer mucho más chico que este soporte, pues tenemos relativamente
## pocas transacciones:
pars <- list(support = 0.05,  target = 'frequent itemsets',
             ext = TRUE)
ap_recetas <- apriori(recetas, parameter = pars)

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 125 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[242 item(s), 2512 transaction(s)] done [0.00s].
sorting and recoding items ... [41 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 done [0.00s].
sorting transactions ... done [0.00s].
writing ... [628 set(s)] done [0.00s].
creating S4 object  ... done [0.00s].

length(ap_recetas)

[1] 628

Vemos los items frecuentes

frecs <- ap_recetas %>% subset(size(.) == 1 ) %>% sort(by = 'support') %>%
 DATAFRAME
DT::datatable(frecs %>% mutate_if(is.numeric, function(x) round(x, 3)))

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Y ahora examinamos combinaciones frecuentes de distintos tamaños

ap_recetas %>% 
  subset(size(.) == 2) %>%
  subset(support > 0.20) %>%
  sort(by = 'support') %>%
  inspect

Incluso hay algunas combinaciones de 4 ingredientes que ocurren con frecuencia alta: estos ingredientes son bases de salsas, combinaciones de condimentos:

ap_recetas %>% 
  subset(size(.) == 4) %>%
  subset(support > 0.10) %>%
  sort(by = 'support') %>%
  inspect

Extracción de reglas

pars <- list(support = 0.01, confidence = 0.10,
             target = 'rules',
             ext = TRUE)
reglas_recetas <- apriori(recetas, parameter = pars)

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 25 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[242 item(s), 2512 transaction(s)] done [0.00s].
sorting and recoding items ... [88 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 7 8 done [0.02s].
writing ... [50181 rule(s)] done [0.01s].
creating S4 object  ... done [0.01s].

agregar_hyperlift <- function(reglas, trans){
  quality(reglas) <- cbind(quality(reglas), 
    hyper_lift = interestMeasure(reglas, measure = "hyperLift", 
    transactions = trans))
  reglas
}
reglas_recetas <- agregar_hyperlift(reglas_recetas, recetas)

Análisis de pares comunes

library(arulesViz)

Loading required package: grid
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

reglas_1 <- subset(reglas_recetas, hyper_lift > 1.1 & support > 0.1 & confidence > 0.40)
length(reglas_1)

[1] 213

reglas_tam_2 <- subset(reglas_1, size(reglas_1)==2)
#inspect(reglas_tam_2 %>% sort(by = 'hyper_lift')) 
plot(reglas_1 %>% subset(support > 0.2), engine = "plotly")

To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
`arrange_()` is deprecated as of dplyr 0.7.0.
Please use `arrange()` instead.
See vignette('programming') for more help
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

library(tidygraph)

Attaching package: ‘tidygraph’

The following object is masked from ‘package:stats’:

    filter

library(ggraph)
frecs <- 
df_reglas <- reglas_tam_2 %>% DATAFRAME %>% rename(from=LHS, to=RHS) %>% data.frame
df_reglas$weight <- log(df_reglas$lift)
graph_1 <- as_tbl_graph(df_reglas) %>%
  mutate(centrality = centrality_degree(mode = "all")) 
set.seed(881)
ggraph(graph_1, layout = 'fr') +
  geom_edge_link(aes(alpha=lift), 
                 colour = 'red',
                 arrow = arrow(length = unit(4, 'mm'))) + 
  geom_node_point(aes(size = centrality, colour = centrality)) + 
  geom_node_text(aes(label = name), size=4,
                 colour = 'gray20', repel=TRUE) +
  theme_graph(base_family = "sans")

reglas_1 <- subset(reglas_recetas, hyper_lift > 1.5 & confidence > 0.1)
length(reglas_1)

[1] 11068

reglas_tam_2 <- subset(reglas_1, size(reglas_1)==2)
length(reglas_tam_2)

[1] 132

library(tidygraph)
library(ggraph)
df_reglas <- reglas_tam_2 %>% DATAFRAME %>% rename(from=LHS, to=RHS) %>% as_data_frame

`as_data_frame()` is deprecated as of tibble 2.0.0.
Please use `as_tibble()` instead.
The signature and semantics have changed, see `?as_tibble`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

df_reglas$weight <- log(df_reglas$hyper_lift)
graph_1 <- as_tbl_graph(df_reglas) %>%
  mutate(centrality = centrality_degree(mode = "all")) 

ggraph(graph_1, layout = 'fr', start.temp=100) +
  geom_edge_link(aes(alpha=lift), 
                 colour = 'red',
                 arrow = arrow(length = unit(4, 'mm'))) + 
  geom_node_point(aes(size = centrality, colour = centrality)) + 
  geom_node_text(aes(label = name), size=4,
                 colour = 'gray20', repel=TRUE) +
  theme_graph(base_family = "sans")

Exportamos para examinar en Gephi:

write_csv(df_reglas %>% rename(source=from, target=to) %>%
            select(-count), 
          path='reglas.csv')

The `path` argument of `write_csv()` is deprecated as of readr 1.4.0.
Please use the `file` argument instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

Nota

La combinación corn y starch puede deberse en parte a una separación incorrecta en el procesamiento de los datos (corn starch o maizena convertido en dos ingredientes, corn y starch):

df_reglas %>% filter(from == "{corn}", to == "{starch}")

La confianza es considerablemente alta, aunque tenemos pocos datos de esta combinación. Podemos examinar algunos ejemplos:

recetas %>% keep(~ "tomato" %in% .x & "corn" %in% .x) %>% head(10)

[[1]]
 [1] "tomato"        "vinegar"       "pork"          "celery_oil"    "leek"         
 [6] "corn"          "black_pepper"  "pepper"        "ginger"        "pea"          
[11] "garlic"        "soybean"       "soy_sauce"     "chicken_broth" "wine"         

[[2]]
 [1] "tomato"     "vinegar"    "pepper"     "celery_oil" "corn"       "cayenne"   
 [7] "pork"       "garlic"     "soybean"    "vegetable"  "coriander"  "rice"      
[13] "soy_sauce" 

[[3]]
[1] "tomato"     "vinegar"    "pork"       "celery_oil" "soy_sauce"  "ginger"    
[7] "garlic"     "sherry"     "corn"      

[[4]]
 [1] "pepper"        "celery_oil"    "starch"        "corn"          "ginger"       
 [6] "garlic"        "soybean"       "tomato"        "vinegar"       "beef"         
[11] "soy_sauce"     "cayenne"       "scallion"      "bell_pepper"   "vegetable_oil"
[16] "rice"          "wine"         

[[5]]
 [1] "tomato"     "vinegar"    "pork"       "celery_oil" "beef"       "soy_sauce" 
 [7] "ginger"     "garlic"     "corn"       "wine"      

[[6]]
 [1] "tomato"      "vinegar"     "pepper"      "lemon_juice" "celery_oil"  "sake"       
 [7] "corn"        "pork"        "ginger"      "honey"       "garlic"      "soybean"    
[13] "rice"        "soy_sauce"  

[[7]]
[1] "tomato"  "garlic"  "onion"   "bacon"   "corn"    "cayenne" "egg"    

[[8]]
 [1] "pork"              "green_bell_pepper" "celery_oil"        "starch"           
 [5] "corn"              "garlic"            "tomato"            "vinegar"          
 [9] "onion"             "soy_sauce"         "cider"             "scallion"         
[13] "celery"            "pineapple"         "vegetable_oil"     "egg"              

[[9]]
 [1] "tomato"       "vinegar"      "pepper"       "celery_oil"   "roasted_pork"
 [6] "soy_sauce"    "ginger"       "honey"        "garlic"       "cinnamon"    
[11] "soybean"      "sherry"       "corn"         "oyster"      

[[10]]
 [1] "cane_molasses" "tomato"        "pork"          "celery_oil"    "vinegar"      
 [6] "soy_sauce"     "pepper"        "ginger"        "garlic"        "corn"